For features selection, LASSO [1]} is used.
It works by constraining the \(\ell ^1\)  norm of the coefficient vector to be less than or equal to a tuning parameter \(\lambda \)  while fitting a linear model to the data.
The predictors that LASSO chooses have a non-zero coefficient for the largest \(\lambda \)  value with the property that the corresponding prediction error is within one standard deviation of the minimum prediction error [2]}, [3]}, [4]}, [5]}, [6]}.
This study performs LASSO feature selection on a fold-by-fold basis during external 10fCV.
Optimal features are picked using LASSO for nine of the ten folds, and the predictions on the tenth fold are performed using these selected features.
This step is iterated such that for every combination of nine folds, an independent set of features is picked.
This usage of LASSO is in contrast to [7]}, where the best features are picked for the entire training set.
Our updated technique ensures that during the 10fCV, LASSO only picks the best predictors based on the training data, and the test set does not affect the models.
This feature selection method is applied to both the O1 and O2 predictor sets.
